Mainly WLAN positioning systems have previously been used commercially, which, on the basis of transmissions from identifiable transmitters (e.g., WLAN routers) as well as the measured reception strength of these transmissions at a receiver, for example, in a positioning device, determine its position. There are in principle two different approaches for this purpose, which are referred to as “position fingerprinting” and as “signal path modeling.”
During “position fingerprinting,” the entire range in which the system is to be used is surveyed prior to using the positioning system, and the reception strengths of all available WLAN networks are entered for each position on a grid. During operation of the positioning system, the reception strengths measured at the positioning device are compared with the reception strengths stored in the database, and the position is calculated on the basis of special algorithms. The simplest possible algorithm defines the position as being that position from the database whose measured reception strengths have the smallest geometric distance from the reception strengths measured during positioning.
During “signal path modeling,” a database having the positions of all WLAN base stations in the positioning area is presupposed, which, among other things, is determinable by measuring the reception strengths in the positioning range as described in U.S. Pat. No. 7,403,762. During positioning, the reception strengths measured at the terminal are converted by a mathematical correlation into distances from the corresponding base station, and the instantaneous position is determined by trilateration based on the known router positions. Trilateration here differs from the conventional triangulation in that it uses only distance information but no angle information.
The accuracy of these two methods depends on the quality of the underlying database because the positioning will never be more accurate than the reference supplied by the database. The errors which may occur in compiling the database are manifold since the measured reception strength depends not only on the distance but, among other things, also on objects in the transmission path, the orientation of the terminal and the time. Furthermore, there is the possibility that, even after the database has been compiled, the positions of the routers may be altered, routers may be removed or new ones added. In order to ensure the functionality of the positioning system even in these cases, the quality of the positioning information of each router is evaluated, for example, as described in U.S. Pat. No. 7,474,897, and thus a decision is made as to whether it may be used for positioning.
U.S. Pat. No. 7,493,127 describes a method for updating the database during operation, in which the consistency of the reception information is tested during each positioning operation, and router positions are adjusted on the basis of the position of the terminal, as determined by other routers, or new router positions may be added in the case of inconsistent or new reception information. These methods ensure that the functionality of the system is not restricted due to changes in the properties of individual routers if other routers, with the aid of which the position may be determined, are present in sufficient numbers.
One possibility for improving the positioning accuracy for pedestrians is dead reckoning by ascertaining the direction and distance from a starting point, also referred to as an inertial navigation system (INS). An INS may be configured on the basis of MEMS sensors, for example. An acceleration sensor is used here to detect a change in position by counting steps in combination with the determination of the step length. In addition, an electronic compass is used, often in combination with a yaw rate sensor, to determine the direction of the horizontal locomotion, while a pressure sensor detects movements in the vertical axis. An improved positioning accuracy through sensor data fusion of INS data and WLAN data is described in “Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning” (F. Evennou, F. Marx, Eurasip Journal on Applied Signal Processing, Hindawi Publishing Corp., New York (2006)) and “WLAN-based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors” (H. Wang, H. Lenz, A. Szabo, J. Bamberger, and U. Hanebeck, Proceedings of Workshop on Positioning, Navigation and Communication, 2007, pp. 1-7, 2007). INS data are used here to determine the position determined via WLAN as a function of time, e.g., with the aid of a particle filter, more accurately than would have been possible without INS.
Furthermore, an INS may be used to simplify the creation of a “location fingerprinting” database as described in “Sensor Data Fusion for Pedestrian Navigation using WLAN and INS” (J. Seitz, L. Patiño-Studencka, B. Schindler, S. Haimerl, J. G. Boronat, S. Meyer and J. Thielecke, in Proceedings of Gyro Technology Symposium 2007, September 2007). For this purpose, the path between two manually input reference positions is interpolated with the aid of the data obtained from INS during the creation of the database, so that fewer manual inputs are needed.
As described in the related art, the accuracy of a positioning system is limited due to the quality of the underlying database with reference positions, which may include the router positions, for example, in the case of WLAN positioning systems. The conventional methods of optimizing the database use only information obtained from the positioning system itself to detect changes in the position references. Thus, for example, the instantaneous position must be determined with the aid of the reception strength of multiple routers at multiple locations in order to be able to determine the position of a new router. Since the position measurement here is subject to a measuring error, the uncertainty in the position of the newly determined router is greater than that of the router used as the reference. Furthermore, systematic errors in the position determination, such as those which may be caused by the reception properties, due to the orientation of the terminal, are propagated directly onto the position of the new router. The quality of the position database therefore declines over time since with each change in router position, the uncertainty with respect to its position becomes greater.
Moreover, new routers may be assigned to a position only in the vicinity of multiple other routers since otherwise it is impossible to determine a reference position. The methods for database optimization may therefore be carried out only in areas having a high router density, but these methods fail on the outskirts of a city and in rural areas, for example.